Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

• Published in: IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
• Main Authors: Xuegang, Luo, Lv, Junrui, Wang, Juan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Datasets; Hyperspectral image restoration; Hyperspectral imaging; Image restoration; Imagery; Mathematical analysis; Mathematical models; Minimization; Noise reduction; Optimization; Regularization; Restoration; Self-similarity; Spectral correlation; tensor ring; Tensors; Visualization; weight parameter optimization
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2022.3199820

Hyperspectral imagery (HSI) restoration is a fundamental problem as a preprocessing step. In this letter, we present a novel auto-weighted nonlocal tensor ring rank minimization (ANTRRM) to reduce noise in HSI. First, nonlocal cuboid tensorization (NCT), built by similar grouping cuboids in HSI data, exploits the nonlocal self-similarity and the spatial-spectral correlation simultaneously. Then, the proposed model introduces nuclear norm (NN) regularization via nonlocal tensor ring with mode-{<inline-formula> <tex-math notation="LaTeX">d </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">l </tex-math></inline-formula>} unfolding. An auto-weighted optimization is employed to represent the different importance of TR unfolding. Finally, the alternating direction method of multipliers (ADMM) scheme is employed to solve the proposed model efficiently. Experiments on two simulation HSIs datasets and a real HSI dataset were carried out, compared with representative approaches in visual and quantitative comparison. The proposed ANTRRM method is superior except in a few cases.